7 papers
RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning
Yichao Zhong, Yidan Lu, Yuhang Lu +9
Elite humanoid soccer shooting requires whole-body stability, high-impulse whole-body interactions, and accuracy to targets. Motion tracking-driven reinforcement learning (RL) prov…
Unified Walking, Running, and Recovery for Humanoids via State-Dependent Adversarial Motion Priors
Yidan Lu, Yichao Zhong, Liu Zhao +2
We propose a unified reinforcement learning framework that enables a single policy to perform walking, running, and fall recovery on the Unitree G1 humanoid robot, validated on phy…
Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game
Jeonghwan Kim, Wontaek Kim, Yidan Lu +9
Recent advances in whole-body robot control have enabled humanoid and legged robots to perform increasingly agile and coordinated motions. However, standardized benchmarks for eval…
Contrastive Representation Learning for Robust Sim-to-Real Transfer of Adaptive Humanoid Locomotion
Yidan Lu, Rurui Yang, Qiran Kou +5
Reinforcement learning has produced remarkable advances in humanoid locomotion, yet a fundamental dilemma persists for real-world deployment: policies must choose between the robus…
FR-Net: Learning Robust Quadrupedal Fall Recovery on Challenging Terrains through Mass-Contact Prediction
Yidan Lu, Yinzhao Dong, Jiahui Zhang +2
Fall recovery for legged robots remains challenging, particularly on complex terrains where traditional controllers fail due to incomplete terrain perception and uncertain interact…
A Survey: Learning Embodied Intelligence from Physical Simulators and World Models
Xiaoxiao Long, Qingrui Zhao, Kaiwen Zhang +15
The pursuit of artificial general intelligence (AGI) has placed embodied intelligence at the forefront of robotics research. Embodied intelligence focuses on agents capable of perc…